7/28/2019 Intrdn. Statistics
1/32
I ntroduction
to Statistics
7/28/2019 Intrdn. Statistics
2/32
A decision of opening up retailsector for FDI cant be taken in a
hurry
7/28/2019 Intrdn. Statistics
3/32
Retailing Sector
Total retail business in India is over Rs 12, 00,000crore, a third of our GDP
After agriculture, it is the largest employer withover 22 million people engaged in it
Throw out the self-employed51 per cent of ourtotal workforce
Unorganized Sector98%
Unorganized sector makes up for 92% of ourentire workforce
Between 2005 and 2010, against the official claimof creating more than 50 million jobs, actuallyonly two million jobs were created
7/28/2019 Intrdn. Statistics
4/32
Retailing Sector
Industry would need additional manpowerof 15 to 30 million in 10 years, if reformsdo happen
The industry currently employs 35 millionpeople
Requirement of an estimated 25 to 30million additional people by 2020
In about 10 years, we would provide directand indirect employment opportunities toapproximately 20,000 people in the storesitself
7/28/2019 Intrdn. Statistics
5/32
What is Statistics?
Science of gathering, analyzing,interpreting, and presenting data
Branch of mathematics
Course of study Facts and figures
A death
Measurement taken on a sample Type of distribution being used to analyze
data
7/28/2019 Intrdn. Statistics
6/32
Population Versus Sample
Populationthe whole- a collection ofpersons, objects, or items of interest
A defined categoryA group of people
A set of objects
Censusgathering data from the entirepopulation
Samplea portion of the wholea subset of the population
7/28/2019 Intrdn. Statistics
7/32
Population
7/28/2019 Intrdn. Statistics
8/32
Population and Census Data
Identifier Color MPG
RD1 Red 12
RD2 Red 10
RD3 Red 13RD4 Red 10
RD5 Red 13
BL1 Blue 27
BL2 Blue 24
GR1 Green 35
GR2 Green 35
GY1 Gray 15GY2 Gray 18
GY3 Gray 17
7/28/2019 Intrdn. Statistics
9/32
Sample and Sample Data
Identifier Color MPG
RD2 Red 10
RD5 Red 13
GR1 Green 35
GY2 Gray 18
7/28/2019 Intrdn. Statistics
10/32
Descriptive vs. Inferential Statistics
Descriptive Statisticsusing data gatheredon a group to describe or reach conclusions
about that same group only
Inferential Statisticsusing sample data toreach conclusions about the population from
which the sample was taken
7/28/2019 Intrdn. Statistics
11/32
Descriptive Statistics Inferential Statistics
Collect
Organize
Summarize
Display
Analyze
Predict and forecast values
of population parametersTest hypotheses aboutvalues of population
parameters
Make decisions
7/28/2019 Intrdn. Statistics
12/32
Descriptive statistics in manufacturing
batteries to make better decisions
total number of worker hours per plant perweek - help management understand laborcosts, work allocation, productivity, etc.
company sales volume of batteries in a year- help management decide if the product isprofitable, how much to advertise in comingyear, compare to costs to determine
profitability. total amount of sulfuric acid purchased per
month for use in battery production. - canbe used by management to study wasted
inventory, scrap, etc.
7/28/2019 Intrdn. Statistics
13/32
Inferential Statistics in manufacturing
batteries to make decisions take a sample of batteries and test them to
determine the average shelf life - use the sampleaverage to reach conclusions about all batteries ofthis type. Management can then make labelingand advertising claims. They can compare thesefigures to the shelf- life of competing batteries.
Take a sample of battery consumers and determinehow many batteries they purchase per year. Inferto the entire population - management can use thisinformation to estimate market potential and
penetration
Interview a random sample of production workersto determine attitude towards companymanagement - management can use this surveyresults to ascertain employee morale and to directefforts towards creating a more positive working
environment which, hopefully, results in greaterproductivity.
7/28/2019 Intrdn. Statistics
14/32
Descriptive statistics in recorded music
industry
total sales of compact discs this week,number of artists under contract to acompany at a given time.
total dollars spent on advertising last monthto promote an album.
number of units produced in a day.
number of retail outlets selling the
company's products.
7/28/2019 Intrdn. Statistics
15/32
Inferential statistics in recorded music
industry
measure the amount spent per month onrecorded music for a few consumers thenuse that figure to infer the amount for thepopulation.
determination of market share for rap musicby randomly selecting a sample of 500purchasers of recorded music.
Determination of top ten single records by
sampling the number of requests at a fewradio stations. Estimation of the average length of a single
recording by taking a sample of records and
measuring them.
7/28/2019 Intrdn. Statistics
16/32
Parameter vs. Statistic
Parameterdescriptive measure of thepopulation
Usually represented by Greek letters
Statisticdescriptive measure of a sampleUsually represented by Roman letters
7/28/2019 Intrdn. Statistics
17/32
Symbols for Population Parameters
denotes population paramet
2
denotes population variance
denotes population standard deviatio
7/28/2019 Intrdn. Statistics
18/32
Symbols for Sample Statistics
x denotes sample mea
2S denotes sample variance
Sdenotes sample standard deviatio
7/28/2019 Intrdn. Statistics
19/32
Process of Inferential Statistics
Population
(parameter )
Sample
x
(statistic)
Calculate x
to estimate
Select a
random sampl
7/28/2019 Intrdn. Statistics
20/32
Variables
Categorical DataQualitative or Nominal Variables
Numerical Data
Discrete or Continuous Variables
7/28/2019 Intrdn. Statistics
21/32
Example 1
Name of Internet provider
Amount of time spent surfing Internet
No. of online purchases made in a month
No. of emails received in a week
7/28/2019 Intrdn. Statistics
22/32
Example 2
Number of telephones per household
Length (in minutes) of longest long-distancecall made per month
Whether there is a telephone line connectedto a computer modem
Whether there is a fax machine in thehousehold
7/28/2019 Intrdn. Statistics
23/32
Example 3
Amount of time spent shopping in thebookstore
Number of textbooks purchased
Academic qualification Gender
7/28/2019 Intrdn. Statistics
24/32
Levels of Data Measurement
NominalLowest level of measurement
Ordinal
ScaleInterval
RatioHighest level of measurement
7/28/2019 Intrdn. Statistics
25/32
Nominal Level Data
Numbers are used to classify or categorizeExample: Employment Classification
1 for Educator 2 for Construction Worker 3 for Manufacturing Worker
Example: Ethnicity 1 for African-American 2 for Anglo-American 3 for Hispanic-American
More Examples: Gender
Religion Geographical location Place of Birth Telephone numbers Employee ID numbers
7/28/2019 Intrdn. Statistics
26/32
Ordinal Level Data Numbers are used to indicate rank or order
Relative magnitude of numbers is meaningful Differences between numbers are not comparable
Example: Ranking productivity of employees
Example: Taste test ranking of three brands of soft drinkExample: Position within an organization
1 for President2 for Vice President3 for Plant Manager
4 for Department Supervisor5 for EmployeeMore Examples:
Computer TutorialMutual Funds
Top Companies
7/28/2019 Intrdn. Statistics
27/32
Example of Ordinal Measurement
fi
n
i
s
h
1
2
3
4
5
6
7/28/2019 Intrdn. Statistics
28/32
Ordinal Data
Faculty and staff should receive preferential
treatment for parking space.
1 2 3 4 5
StronglyAgree
Agree StronglyDisagree
DisagreeNeutral
7/28/2019 Intrdn. Statistics
29/32
Interval Level Data
Distances between consecutive integers are equal Relative magnitude of numbers is meaningful Differences between numbers are comparable Location of origin, zero, is arbitrary Vertical intercept of unit of measure transform
function is not zeroExample: Fahrenheit TemperatureMore Examples:
Percentage Change in employmentPercentage Return on a stock
Dollar Change in Stock Pricey = b + ax
7/28/2019 Intrdn. Statistics
30/32
Ratio Level Data
Highest level of measurement Relative magnitude of numbers is meaningful Differences between numbers are comparable Location of origin, zero, is absolute (natural)
Examples: Height, Weight, and VolumeExample: Monetary Variables, such as Profit andLoss, Revenues, and Expenses
Example: Financial ratios, such as P/E Ratio,Inventory Turnover, and Quick Ratio.
More Examples: Number of trucks sold, Complaints
per 1,000 customers, Number of employeesy = ax
7/28/2019 Intrdn. Statistics
31/32
Usage Potential of Various
Levels of Data
Nominal
Ordinal
Interval
Ratio
7/28/2019 Intrdn. Statistics
32/32
Data Level, Operations,
and Statistical Methods
Data Level
Nominal
Ordinal
Interval
Ratio
Meaningful Operations
Classifying and Counting
All of the above plus Ranking
All of the above plus Addition,
Subtraction, Multiplication, and
Division
All of the above
StatisticalMethods
Nonparametric
Nonparametric
Parametric
Parametric